1 Raw reads and preprocessing

1.1 Raw data / run summary

Making sure that the two sequencing runs are not too different from each other. In general it seems like Run2 had a higher proportion of reads pass filter but not more overall reads ## Mock community check Next we check that the Mock community is well covered in both runs. Mock1 is from Run1 and Mock2 is from Run2. Looks like there’s very low contamination of other sequences in the mock samples. However, there is some slight difference in the ordering of mock abundances, but this variation is similar to previous runs.

## # A tibble: 2 × 3
## # Groups:   sample [2]
##   sample    Correct     Wrong
##   <chr>       <dbl>     <dbl>
## 1 KLF_Mock1   0.999 0.00110  
## 2 KLF_Mock2   1.00  0.0000472

Now that mock is checked out, we can remove it from the data.

1.2 Removing contaminant ASVs with decontam

We will use decontam and the negative controls (both extraction negatives and PCR negatives) to filter out potential contaminant sequences by frequency of appearance in the negative controls.

## 
## FALSE  TRUE 
## 11676    62

Below is the taxonomy of the ASVs that were identified as contaminants.

Kingdom Phylum Class Order Family Genus Species
ASV283 Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Xanthobacteraceae Bradyrhizobium NA
ASV966 Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Beijerinckiaceae Bosea NA
ASV752 Bacteria Proteobacteria Alphaproteobacteria Caulobacterales Caulobacteraceae Brevundimonas albigilva/nasdae/vesicularis
ASV5290 Bacteria Actinobacteriota Actinobacteria Micrococcales Micrococcaceae Rothia dentocariosa
ASV861 Bacteria Actinobacteriota Actinobacteria Corynebacteriales Mycobacteriaceae Mycobacterium NA
ASV169 Bacteria Actinobacteriota Actinobacteria Corynebacteriales Corynebacteriaceae Lawsonella clevelandensis
ASV1193 Bacteria Actinobacteriota Actinobacteria Corynebacteriales Corynebacteriaceae Corynebacterium aurimucosum/pseudogenitalium/tuberculostearicum
ASV492 Bacteria Actinobacteriota Actinobacteria Propionibacteriales Nocardioidaceae Nocardioides jensenii
ASV485 Bacteria Actinobacteriota Actinobacteria Propionibacteriales Propionibacteriaceae Cutibacterium acnes/avidum
ASV630 Bacteria Deinococcota Deinococci Thermales Thermaceae Thermus amyloliquefaciens/scotoductus
ASV874 Bacteria Cyanobacteria Cyanobacteriia Chloroplast NA NA NA
ASV890 Bacteria Fusobacteriota Fusobacteriia Fusobacteriales Leptotrichiaceae Leptotrichia shahii/wadei
ASV3528 Bacteria Fusobacteriota Fusobacteriia Fusobacteriales Fusobacteriaceae Fusobacterium nucleatum
ASV2065 Bacteria Firmicutes Clostridia Clostridiales Oxobacteraceae Oxobacter NA
ASV322 Bacteria Firmicutes Clostridia Clostridia_or Hungateiclostridiaceae Ruminiclostridium NA
ASV2038 Bacteria Firmicutes Bacilli Lactobacillales Leuconostocaceae Leuconostoc NA
ASV51 Bacteria Firmicutes Bacilli Lactobacillales Lactobacillaceae Lactobacillus NA
ASV1457 Bacteria Firmicutes Bacilli Lactobacillales Streptococcaceae Streptococcus anginosus/intermedius
ASV2851 Bacteria Firmicutes Bacilli Staphylococcales Gemellaceae Gemella NA
ASV19 Bacteria Firmicutes Bacilli Staphylococcales Staphylococcaceae Staphylococcus NA
ASV464 Bacteria Firmicutes Bacilli Bacillales Planococcaceae Planococcus NA
ASV2251 Bacteria Firmicutes Bacilli Bacillales Planococcaceae Chungangia NA
ASV74 Bacteria Firmicutes Bacilli Bacillales Sporolactobacillaceae Sporolactobacillus NA
ASV379 Bacteria Firmicutes Bacilli Bacillales Bacillaceae NA NA
ASV316 Bacteria Firmicutes Bacilli Bacillales Sporolactobacillaceae Terrilactibacillus laevilacticus
ASV2326 Bacteria Firmicutes Negativicutes Veillonellales-Selenomonadales Veillonellaceae Veillonella parvula
ASV4175 Bacteria Proteobacteria NA NA NA NA NA
ASV1270 Bacteria Proteobacteria Alphaproteobacteria Rickettsiales Mitochondria NA NA
ASV474 Bacteria Proteobacteria Alphaproteobacteria Rickettsiales Mitochondria NA NA
ASV1350 Bacteria Proteobacteria Alphaproteobacteria Rickettsiales Mitochondria NA NA
ASV1372 Bacteria Proteobacteria Alphaproteobacteria Rickettsiales Mitochondria NA NA
ASV175 Bacteria Proteobacteria Alphaproteobacteria Rickettsiales Mitochondria NA NA
ASV633 Bacteria Proteobacteria Alphaproteobacteria Rickettsiales Mitochondria NA NA
ASV4363 Bacteria Bacteroidota Bacteroidia Chitinophagales Chitinophagaceae Vibrionimonas NA
ASV138 Bacteria Bacteroidota Bacteroidia Flavobacteriales Weeksellaceae Cloacibacterium caeni/normanense/rupense
ASV4176 Bacteria Bacteroidota Bacteroidia Flavobacteriales Flavobacteriaceae Zunongwangia NA
ASV916 Bacteria Bacteroidota Bacteroidia Chitinophagales Saprospiraceae Portibacter NA
ASV4268 Bacteria Proteobacteria Gammaproteobacteria Cellvibrionales Cellvibrionaceae Cellvibrio NA
ASV431 Bacteria Proteobacteria Gammaproteobacteria Alteromonadales Alteromonadaceae Glaciecola NA
ASV258 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas NA
ASV3377 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas NA
ASV335 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas NA
ASV1488 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas NA
ASV2930 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas NA
ASV39 Bacteria Proteobacteria Gammaproteobacteria Cellvibrionales Cellvibrionaceae NA NA
ASV1112 Bacteria Proteobacteria Gammaproteobacteria Gammaproteobacteria_Incertae_Sedis Unknown_Family Acidibacter NA
ASV105 Bacteria Proteobacteria Gammaproteobacteria Burkholderiales Comamonadaceae Delftia NA
ASV2151 Bacteria Proteobacteria Gammaproteobacteria Burkholderiales Comamonadaceae Acidovorax NA
ASV186 Bacteria Proteobacteria Gammaproteobacteria Burkholderiales Burkholderiaceae Ralstonia NA
ASV55 Bacteria Proteobacteria Gammaproteobacteria Burkholderiales Alcaligenaceae Achromobacter NA
ASV214 Bacteria Proteobacteria Gammaproteobacteria Burkholderiales Neisseriaceae Neisseria flavescens/perflava
ASV465 Bacteria Proteobacteria Gammaproteobacteria Enterobacterales Yersiniaceae Yersinia NA
ASV81 Bacteria Proteobacteria Gammaproteobacteria Enterobacterales Yersiniaceae NA NA
ASV36 Bacteria Proteobacteria Gammaproteobacteria Enterobacterales Yersiniaceae Serratia NA
ASV7 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Moraxellaceae Acinetobacter NA
ASV198 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Moraxellaceae Enhydrobacter NA
ASV58 Bacteria Proteobacteria Gammaproteobacteria Xanthomonadales Xanthomonadaceae Stenotrophomonas maltophilia
ASV2442 Bacteria Proteobacteria Gammaproteobacteria Xanthomonadales Xanthomonadaceae Stenotrophomonas NA
ASV971 Bacteria Proteobacteria Alphaproteobacteria Caulobacterales Hyphomonadaceae NA NA
ASV161 Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Mesorhizobium huakuii/loti
ASV326 Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Pseudahrensia NA
ASV428 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Yoonia-Loktanella NA

Here’s a graph of the ASV prevalence in true vs negative control samples. Highlighted in blue are the ones identified as contaminants.

1.3 Chloroplasts

I also want to remove the chloroplasts from the samples. However, I want to make a record of the chloroplast abundance in the metadata of the sample, because it may be useful information later.

Removing chloroplasts, contaminants, and non-bacterial/archaeal ASVs How do the read counts look after we remove the chloroplast samples and the putative contaminants?

1.4 Cleaned up stats

In the table below nochim is the number of raw reads that passed dada2 filtering. prop.retained is the proportion that passed dada2 filtering. reads.clean is the number of reads we currently have and final.prop is the final proportion of reads pass all the filtering (dada2 and what we just did)
skim_type skim_variable n_missing complete_rate numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
numeric nochim 0 1 9.169762e+04 2.404930e+04 3.657100e+04 7.500600e+04 9.317500e+04 1.059280e+05 2.149200e+05 ▂▇▃▁▁
numeric prop.retained 0 1 7.731884e-01 7.250860e-02 4.670639e-01 7.423043e-01 7.793883e-01 8.306927e-01 8.716865e-01 ▁▁▁▆▇
numeric reads.clean 0 1 8.481864e+04 2.627104e+04 6.412000e+03 6.941350e+04 8.812500e+04 9.889350e+04 2.146840e+05 ▂▇▇▁▁
numeric final.prop 0 1 7.116660e-01 1.197761e-01 5.979110e-02 6.905133e-01 7.497528e-01 7.826109e-01 8.477940e-01 ▁▁▁▂▇

The script preprocessing.R will generate the cleaned data that will be used in the subsequent analysis. After this, I will load the preprocessed .rds files

2 Diversity analysis

2.1 Alpha diversity

Comparing wild and F2 killifish alpha diversity. Black dots represent the water at each site at the time of sampling. Water for the captive killifish is not available.

### Alpha diversity significance tests

Pairwise t-tests show that NBH wild fish have significantly different diversity values than all other fish types while SC wild, SC F2, and NBH F2 fish have similar measurements. One exception is that SC wild has a higher Simpson measurement than NBH F2.

2.2 Beta dispersion

Subsetting to just the gut samples, now we look at the beta dispersion/diversity between sites and between F2 and wild type

Tukey’s HSD test shows that all pairwise comparisons of beta dispersion between fish types are significantly different except for between the F2 fish.

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = distances ~ group, data = df)
## 
## $group
##                                                      diff         lwr
## New Bedford Harbor wild-New Bedford Harbor F2  0.53786755  0.45527199
## Scorton Creek F2-New Bedford Harbor F2         0.03212203 -0.08091673
## Scorton Creek wild-New Bedford Harbor F2       0.27806619  0.19547063
## Scorton Creek F2-New Bedford Harbor wild      -0.50574551 -0.62021219
## Scorton Creek wild-New Bedford Harbor wild    -0.25980136 -0.34434061
## Scorton Creek wild-Scorton Creek F2            0.24594415  0.13147747
##                                                      upr     p adj
## New Bedford Harbor wild-New Bedford Harbor F2  0.6204631 0.0000000
## Scorton Creek F2-New Bedford Harbor F2         0.1451608 0.8810824
## Scorton Creek wild-New Bedford Harbor F2       0.3606617 0.0000000
## Scorton Creek F2-New Bedford Harbor wild      -0.3912788 0.0000000
## Scorton Creek wild-New Bedford Harbor wild    -0.1752621 0.0000000
## Scorton Creek wild-Scorton Creek F2            0.3604108 0.0000007

3 Ordination

3.1 PCoA of all samples

I attempted to do an NMDS ordination but metaMDS would not converge. So I created a PCoA using Bray-Curtis dissimilarity

3.2 Ordination of just wild

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## ... New best solution
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## ... New best solution
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## ... Procrustes: rmse 0.01232162  max resid 0.06891814 
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## ... New best solution
## ... Procrustes: rmse 5.19067e-05  max resid 0.0002293289 
## ... Similar to previous best
## *** Solution reached

3.3 PERMANOVA of wild and F2 fish

Interestingly a PERMANOVA finds that the two F2 populations are significantly different

## Permutation test for adonis under reduced model
## Marginal effects of terms
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = distance(ps.wild.rel, "bray") ~ site + weight.g + length.cm, data = as(sample_data(ps.wild.rel), "data.frame"), by = "margin")
##           Df SumOfSqs      R2       F Pr(>F)    
## site       1   5.3728 0.20073 19.8463  0.001 ***
## weight.g   1   0.4256 0.01590  1.5722  0.109    
## length.cm  1   0.3991 0.01491  1.4742  0.137    
## Residual  76  20.5748 0.76868                   
## Total     79  26.7663 1.00000                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation test for adonis under reduced model
## Marginal effects of terms
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = distance(ps.f2.rel, "bray") ~ site + weight.g + length.cm + sex, data = as(sample_data(ps.f2.rel), "data.frame"), by = "margin")
##           Df SumOfSqs      R2      F Pr(>F)   
## site       1  0.05604 0.07898 5.2105  0.003 **
## weight.g   1  0.01619 0.02282 1.5054  0.203   
## length.cm  1  0.01157 0.01631 1.0760  0.342   
## sex        1  0.00953 0.01344 0.8865  0.469   
## Residual  54  0.58074 0.81856                 
## Total     58  0.70946 1.00000                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

3.4 PCA Biplot

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within the wild samples, ASV2, ASV3, and ASV5 structure the populations

4 ASV differences

4.1 Overlap of ASVs

I was curious how many ASVs were unique to each environment/fish type: Scorton Creek wild, New Bedford Harbor wild, and the respective F2 fish

## [1] 1

Venn Diagram

F2 fish share 20% of their ASVs while wild fish share 6.3% of their ASVs.

4.2 Core taxa - present in all fish types

Core taxa in wild type are either Vibrionaceae or Mycoplasma while core taxa in F2 are Lactobacillales

Core Lactobacillales ASVs:

Order Family Genus Species
ASV1 Lactobacillales Enterococcaceae Enterococcus NA
ASV14 Lactobacillales Streptococcaceae Lactococcus formosensis/garvieae
ASV16 Lactobacillales Leuconostocaceae Weissella NA
ASV45 Lactobacillales Streptococcaceae Lactococcus garvieae/lactis
ASV1881 Lactobacillales Leuconostocaceae Leuconostoc NA
Core Vibrionales ASVs:
Order Family Genus Species
ASV2 Vibrionales Vibrionaceae NA NA
ASV5 Vibrionales Vibrionaceae Vibrio NA
ASV13 Vibrionales Vibrionaceae Vibrio NA
ASV18 Vibrionales Vibrionaceae Aliivibrio NA
ASV43 Vibrionales Vibrionaceae Photobacterium NA
Core Mycoplasmatales ASVs:
Order Family Genus Species
ASV3 Mycoplasmatales Mycoplasmataceae Mycoplasma NA
ASV10 Mycoplasmatales Mycoplasmataceae NA NA
ASV26 Mycoplasmatales Mycoplasmataceae Mycoplasma NA

There are still some of these major groups that are not part of the core that are super abundant in some samples. The following bar chart includes all ASVs from the three main orders, not just the core ASVs. Notice the Lactobacillales that appear in a few of the NBH wild fish and the additional Vibrionales in the SC wild fish.

4.2.1 What are the top most abundant orders in killifish guts?

I wanted to know what were the most dominant orders in each fish type. So I found the top 5 average relative abundance of order for each fish type and plotted their abundances. 15 Orders had high average relative abundance in at least one fish type, but some were clearly due to a couple of outlier fish.

I removed those orders in which only a few fish showed high relative abundance. Now the number of orders is down to 5 and it’s much easier to interpret. Wild fish are dominated by Vibrio and Mycoplasmatales while F2 fish are dominated by Lactobacillales and somewhat by Clostridiales and Cyanobacteriales. I found it interesting that SC wild fish have huge amounts to Vibrio but also huge amounts of Myco, while NBH wild just has a variable amount of vibrio and a small amount of myco. I wonder if the Myco and Vibrio in SC are negatively correlated. For more, see the vibrio section.

4.3 Prevalence and abundance

TBD

4.4 Differentially abundant ASVs

Differentially abundant ASVs between wild and F2

Differentially abundant taxa between F2 SC and NBH fish

Differentially abundant ASVs between NBH wild and SC wild

4.4.1 Abundance of significant taxa in the seawater

Are these taxa just enriched in the seawater of the respective sites? Only a small fraction of the significant ASVs are more abundant in SC water if they were enriched in the guts or less abundant in SC water if they were depleted in guts.
ASV SC_enriched Taxonomy NBH_abund SC_abund expected
ASV104 FALSE g:Cyanobium_PCC-6307 0.0039700 0.0424259 no
ASV133 FALSE f:Intrasporangiaceae 0.0000000 0.0000000 no
ASV145 FALSE g:Exiguobacterium 0.0000000 0.0000000 no
ASV17 FALSE f:Vibrionaceae 0.0000000 0.0000000 no
ASV2 TRUE f:Vibrionaceae 0.0014670 0.0013125 no
ASV201 FALSE Ahrensia kielensis/marina 0.0000000 0.0000000 no
ASV221 FALSE Sulfitobacter dubius 0.0000000 0.0000000 no
ASV225 FALSE Labrenzia aggregata 0.0000000 0.0000000 no
ASV227 FALSE Tropicimonas aquimaris 0.0000000 0.0000000 no
ASV23 FALSE g:Aliivibrio 0.0000000 0.0000000 no
ASV238 FALSE f:Rhizobiaceae 0.0000000 0.0000000 no
ASV24 FALSE f:Desulfovibrionaceae 0.0000000 0.0000000 no
ASV241 FALSE Ilumatobacter nonamiensis 0.0000000 0.0000000 no
ASV26 TRUE g:Mycoplasma 0.0000000 0.0000000 no
ASV261 FALSE f:Nitriliruptoraceae 0.0000000 0.0000000 no
ASV28 FALSE Psychromonas kaikoae/marina 0.0000000 0.0012025 no
ASV33 FALSE g:Brevinema 0.0000000 0.0000000 no
ASV43 FALSE g:Photobacterium 0.0003281 0.0008345 no
ASV47 FALSE g:Candidatus_Bacilloplasma 0.0000000 0.0000000 no
ASV5 FALSE g:Vibrio 0.0031767 0.0037468 no
ASV503 FALSE g:Planococcus 0.0000000 0.0000000 no
ASV513 FALSE f:Caldilineaceae 0.0000000 0.0000000 no
ASV52 FALSE g:Ascidiaceihabitans 0.0000000 0.0000000 no
ASV616 FALSE c:KD4-96 0.0000000 0.0000000 no
ASV67 FALSE Yangia pacifica 0.0000000 0.0000000 no
ASV79 TRUE g:Propionigenium 0.0000000 0.0000000 no
ASV82 FALSE g:Rubripirellula 0.0000000 0.0003079 no
ASV91 FALSE Enterococcus cecorum 0.0000000 0.0000000 no
ASV10 TRUE f:Mycoplasmataceae 0.0000000 0.0000494 yes
ASV106 FALSE c:Gammaproteobacteria 0.0004223 0.0000000 yes
ASV113 FALSE g:Sulfurovum 0.0004773 0.0000000 yes
ASV22 FALSE Lactobacillus intermedius 0.0001000 0.0000000 yes
ASV229 FALSE f:Gimesiaceae 0.0003310 0.0000000 yes
ASV246 FALSE g:Halioglobus 0.0008910 0.0000000 yes
ASV27 TRUE g:Marinobacterium 0.0000000 0.0008759 yes
ASV30 FALSE g:Marivita 0.0001448 0.0000000 yes
ASV328 FALSE g:Methyloceanibacter 0.0000521 0.0000000 yes
ASV64 FALSE f:Desulfocapsaceae 0.0006620 0.0000000 yes
ASV80 FALSE g:Roseobacter 0.0001043 0.0000000 yes
ASV85 TRUE g:Thiohalocapsa 0.0000000 0.0003383 yes

5 Vibrionales

In the core taxa section, I found that Vibrionales is highly abundant in wild fish, and particularly in Scorton Creek wild fish. In the differential abundance analysis, I found that it’s a specific vibrio ASV (ASV2) that is enriched in SC fish (both wild and F2) while NBH wild fish are enriched in different vibrio taxa. I think this difference warrants further investigation.

5.1 Vibrionales differences between the wild fish

I looked at the overlap in vibrio ASVs between the two fish populations.

Scorton Creek fish have 77 Vibrio ASVs, New Bedford fish have 115 Vibrio ASVs, and they share 41 Vibrio ASVs. For clarity, I only plotted the ASVs which ever reach a relative abundance above 1%. In SC ASV2 is dominant, but in NBH ASV2 and ASV5 are about equally dominant

5.2 Tree of Vibrios

## Vibrio corrplot

6 Microbial co-occurence networks

TBD

6.1 In wild fish

I found that SC fish had high abundances of both vibrios and mycoplasmatales and wondered if they were negatively correlated overall and if Vibrio and Mycoplasmatales collectively structure wild (or just SC) gut microbiomes.

6.2 In F2 fish

6.3 Host AHR genotype distributions

Let’s look at the distribution of host genotypes. I think it’s interesting that the distribution of genotypes is so similar for the wild fish but different in the F2 that were common garden raised. Fish with genotype “none” did not have a band wither either set of primer. I have re-extracted DNA and re-run the primers for the wild fish, which decreased the number of “none” genotypes, but have not done so yet for the F2 fish, hence the greater number of “none” fish in F2.

6.4 Wild fish microbiome correlation with AHR genotype

Not looking great visually

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## ... New best solution
## ... Procrustes: rmse 0.05798644  max resid 0.1929456 
## Run 36 stress 0.2094419 
## Run 37 stress 0.2310053 
## Run 38 stress 0.2393703 
## Run 39 stress 0.2055215 
## ... Procrustes: rmse 0.01202388  max resid 0.06646121 
## Run 40 stress 0.2135558 
## Run 41 stress 0.2183995 
## Run 42 stress 0.2282661 
## Run 43 stress 0.2246887 
## Run 44 stress 0.2382327 
## Run 45 stress 0.2077069 
## Run 46 stress 0.2362343 
## Run 47 stress 0.2363008 
## Run 48 stress 0.2051982 
## ... New best solution
## ... Procrustes: rmse 0.008310794  max resid 0.04943289 
## Run 49 stress 0.2127829 
## Run 50 stress 0.2109343 
## Run 51 stress 0.2417562 
## Run 52 stress 0.2330226 
## Run 53 stress 0.2264774 
## Run 54 stress 0.2237656 
## Run 55 stress 0.2470312 
## Run 56 stress 0.2254736 
## Run 57 stress 0.224659 
## Run 58 stress 0.2123271 
## Run 59 stress 0.2303092 
## Run 60 stress 0.2064145 
## Run 61 stress 0.2379212 
## Run 62 stress 0.2089927 
## Run 63 stress 0.2055712 
## ... Procrustes: rmse 0.01067262  max resid 0.07392896 
## Run 64 stress 0.2408523 
## Run 65 stress 0.2311619 
## Run 66 stress 0.2111465 
## Run 67 stress 0.2051091 
## ... New best solution
## ... Procrustes: rmse 0.01072919  max resid 0.05670056 
## Run 68 stress 0.2456645 
## Run 69 stress 0.2207193 
## Run 70 stress 0.2143196 
## Run 71 stress 0.2217399 
## Run 72 stress 0.2265254 
## Run 73 stress 0.2349678 
## Run 74 stress 0.2193564 
## Run 75 stress 0.2367495 
## Run 76 stress 0.2171829 
## Run 77 stress 0.2130117 
## Run 78 stress 0.2218182 
## Run 79 stress 0.241517 
## Run 80 stress 0.2296554 
## Run 81 stress 0.2104327 
## Run 82 stress 0.2311274 
## Run 83 stress 0.2328461 
## Run 84 stress 0.2142026 
## Run 85 stress 0.2243672 
## Run 86 stress 0.2101052 
## Run 87 stress 0.2284881 
## Run 88 stress 0.2263735 
## Run 89 stress 0.220634 
## Run 90 stress 0.2123613 
## Run 91 stress 0.2314886 
## Run 92 stress 0.2439834 
## Run 93 stress 0.2101491 
## Run 94 stress 0.2094436 
## Run 95 stress 0.2434325 
## Run 96 stress 0.211102 
## Run 97 stress 0.2123345 
## Run 98 stress 0.2135842 
## Run 99 stress 0.2261107 
## Run 100 stress 0.2061835 
## Run 101 stress 0.2224873 
## Run 102 stress 0.2090026 
## Run 103 stress 0.2405005 
## Run 104 stress 0.2308013 
## Run 105 stress 0.2264014 
## Run 106 stress 0.2401571 
## Run 107 stress 0.2151815 
## Run 108 stress 0.2058128 
## Run 109 stress 0.2049708 
## ... New best solution
## ... Procrustes: rmse 0.009932977  max resid 0.07604792 
## Run 110 stress 0.2366083 
## Run 111 stress 0.2057106 
## Run 112 stress 0.2418679 
## Run 113 stress 0.2065009 
## Run 114 stress 0.2083755 
## Run 115 stress 0.2316151 
## Run 116 stress 0.2238095 
## Run 117 stress 0.213772 
## Run 118 stress 0.213625 
## Run 119 stress 0.2469097 
## Run 120 stress 0.2121885 
## Run 121 stress 0.2258479 
## Run 122 stress 0.2116042 
## Run 123 stress 0.209135 
## Run 124 stress 0.2087854 
## Run 125 stress 0.2076727 
## Run 126 stress 0.2131979 
## Run 127 stress 0.2051982 
## ... Procrustes: rmse 0.008986036  max resid 0.05684277 
## Run 128 stress 0.2389542 
## Run 129 stress 0.2080353 
## Run 130 stress 0.2086044 
## Run 131 stress 0.2259291 
## Run 132 stress 0.205109 
## ... Procrustes: rmse 0.009946142  max resid 0.07617127 
## Run 133 stress 0.2272624 
## Run 134 stress 0.2140034 
## Run 135 stress 0.2281391 
## Run 136 stress 0.2231591 
## Run 137 stress 0.2051091 
## ... Procrustes: rmse 0.009933417  max resid 0.07600058 
## Run 138 stress 0.2363222 
## Run 139 stress 0.244955 
## Run 140 stress 0.2049372 
## ... New best solution
## ... Procrustes: rmse 0.005604074  max resid 0.03200545 
## Run 141 stress 0.2121307 
## Run 142 stress 0.2054607 
## Run 143 stress 0.2087408 
## Run 144 stress 0.2075435 
## Run 145 stress 0.2142665 
## Run 146 stress 0.2153982 
## Run 147 stress 0.213823 
## Run 148 stress 0.2134526 
## Run 149 stress 0.23527 
## Run 150 stress 0.2082867 
## Run 151 stress 0.2061833 
## Run 152 stress 0.2080253 
## Run 153 stress 0.2420936 
## Run 154 stress 0.215156 
## Run 155 stress 0.2250262 
## Run 156 stress 0.2331436 
## Run 157 stress 0.2355074 
## Run 158 stress 0.2089828 
## Run 159 stress 0.21423 
## Run 160 stress 0.2080441 
## Run 161 stress 0.2289173 
## Run 162 stress 0.2389676 
## Run 163 stress 0.2121632 
## Run 164 stress 0.2305304 
## Run 165 stress 0.2362499 
## Run 166 stress 0.223972 
## Run 167 stress 0.212958 
## Run 168 stress 0.2125874 
## Run 169 stress 0.2105423 
## Run 170 stress 0.2061777 
## Run 171 stress 0.2321443 
## Run 172 stress 0.213356 
## Run 173 stress 0.2247826 
## Run 174 stress 0.2290385 
## Run 175 stress 0.2239726 
## Run 176 stress 0.2086436 
## Run 177 stress 0.215454 
## Run 178 stress 0.2071893 
## Run 179 stress 0.208864 
## Run 180 stress 0.2056064 
## Run 181 stress 0.2065009 
## Run 182 stress 0.2438026 
## Run 183 stress 0.2372219 
## Run 184 stress 0.2051592 
## ... Procrustes: rmse 0.007769127  max resid 0.05735283 
## Run 185 stress 0.2093274 
## Run 186 stress 0.2137622 
## Run 187 stress 0.2064274 
## Run 188 stress 0.232173 
## Run 189 stress 0.2052271 
## ... Procrustes: rmse 0.008528714  max resid 0.0572622 
## Run 190 stress 0.2083337 
## Run 191 stress 0.2067813 
## Run 192 stress 0.2377806 
## Run 193 stress 0.2074037 
## Run 194 stress 0.2061733 
## Run 195 stress 0.2371606 
## Run 196 stress 0.2442204 
## Run 197 stress 0.2394952 
## Run 198 stress 0.2311109 
## Run 199 stress 0.2101553 
## Run 200 stress 0.2246602 
## Run 201 stress 0.2294761 
## Run 202 stress 0.2402689 
## Run 203 stress 0.2054932 
## Run 204 stress 0.2306398 
## Run 205 stress 0.2299493 
## Run 206 stress 0.2312014 
## Run 207 stress 0.2349412 
## Run 208 stress 0.2049352 
## ... New best solution
## ... Procrustes: rmse 0.003688029  max resid 0.02267798 
## Run 209 stress 0.2326277 
## Run 210 stress 0.2439299 
## Run 211 stress 0.2079659 
## Run 212 stress 0.2168838 
## Run 213 stress 0.2200503 
## Run 214 stress 0.2368714 
## Run 215 stress 0.2316767 
## Run 216 stress 0.2232205 
## Run 217 stress 0.2455869 
## Run 218 stress 0.2194556 
## Run 219 stress 0.2414474 
## Run 220 stress 0.2105457 
## Run 221 stress 0.2289407 
## Run 222 stress 0.244274 
## Run 223 stress 0.2151952 
## Run 224 stress 0.2306842 
## Run 225 stress 0.2153257 
## Run 226 stress 0.2139287 
## Run 227 stress 0.2323504 
## Run 228 stress 0.2158736 
## Run 229 stress 0.2096629 
## Run 230 stress 0.2136318 
## Run 231 stress 0.2067735 
## Run 232 stress 0.2088582 
## Run 233 stress 0.2256605 
## Run 234 stress 0.2366392 
## Run 235 stress 0.2096705 
## Run 236 stress 0.2086463 
## Run 237 stress 0.2377577 
## Run 238 stress 0.2166291 
## Run 239 stress 0.2259506 
## Run 240 stress 0.2182905 
## Run 241 stress 0.2368364 
## Run 242 stress 0.2101351 
## Run 243 stress 0.2359625 
## Run 244 stress 0.2391847 
## Run 245 stress 0.2106997 
## Run 246 stress 0.2365985 
## Run 247 stress 0.2110305 
## Run 248 stress 0.2388789 
## Run 249 stress 0.2052727 
## ... Procrustes: rmse 0.01003568  max resid 0.06911455 
## Run 250 stress 0.2371073 
## Run 251 stress 0.2140436 
## Run 252 stress 0.2439886 
## Run 253 stress 0.2064275 
## Run 254 stress 0.2300807 
## Run 255 stress 0.2369869 
## Run 256 stress 0.2360284 
## Run 257 stress 0.2062234 
## Run 258 stress 0.2315481 
## Run 259 stress 0.230453 
## Run 260 stress 0.2261407 
## Run 261 stress 0.2078695 
## Run 262 stress 0.2147679 
## Run 263 stress 0.2263779 
## Run 264 stress 0.2391057 
## Run 265 stress 0.2270544 
## Run 266 stress 0.2330683 
## Run 267 stress 0.2265041 
## Run 268 stress 0.2331798 
## Run 269 stress 0.2123614 
## Run 270 stress 0.209969 
## Run 271 stress 0.2159813 
## Run 272 stress 0.2228965 
## Run 273 stress 0.2064145 
## Run 274 stress 0.205346 
## ... Procrustes: rmse 0.00961791  max resid 0.05734518 
## Run 275 stress 0.2354981 
## Run 276 stress 0.2279352 
## Run 277 stress 0.2101765 
## Run 278 stress 0.2103134 
## Run 279 stress 0.2151676 
## Run 280 stress 0.2407643 
## Run 281 stress 0.2134605 
## Run 282 stress 0.2101562 
## Run 283 stress 0.2093177 
## Run 284 stress 0.2286462 
## Run 285 stress 0.2115182 
## Run 286 stress 0.2065584 
## Run 287 stress 0.2278567 
## Run 288 stress 0.2084341 
## Run 289 stress 0.2119909 
## Run 290 stress 0.2067651 
## Run 291 stress 0.2076995 
## Run 292 stress 0.228722 
## Run 293 stress 0.2222122 
## Run 294 stress 0.2292178 
## Run 295 stress 0.2318521 
## Run 296 stress 0.2143546 
## Run 297 stress 0.2076629 
## Run 298 stress 0.2463396 
## Run 299 stress 0.2455455 
## Run 300 stress 0.2389751 
## Run 301 stress 0.2088411 
## Run 302 stress 0.2311292 
## Run 303 stress 0.2101996 
## Run 304 stress 0.2339335 
## Run 305 stress 0.2302411 
## Run 306 stress 0.2133356 
## Run 307 stress 0.2256722 
## Run 308 stress 0.2116705 
## Run 309 stress 0.2406451 
## Run 310 stress 0.205109 
## ... Procrustes: rmse 0.006870954  max resid 0.04731704 
## Run 311 stress 0.2272904 
## Run 312 stress 0.205109 
## ... Procrustes: rmse 0.00687886  max resid 0.04744695 
## Run 313 stress 0.2051597 
## ... Procrustes: rmse 0.007793126  max resid 0.04712099 
## Run 314 stress 0.2141428 
## Run 315 stress 0.2332293 
## Run 316 stress 0.2085 
## Run 317 stress 0.2132155 
## Run 318 stress 0.2108137 
## Run 319 stress 0.2154058 
## Run 320 stress 0.2106114 
## Run 321 stress 0.2076353 
## Run 322 stress 0.2326997 
## Run 323 stress 0.2301842 
## Run 324 stress 0.2094551 
## Run 325 stress 0.206925 
## Run 326 stress 0.2131202 
## Run 327 stress 0.2116249 
## Run 328 stress 0.2092726 
## Run 329 stress 0.2101594 
## Run 330 stress 0.2055131 
## Run 331 stress 0.2123732 
## Run 332 stress 0.2095269 
## Run 333 stress 0.2431066 
## Run 334 stress 0.2442961 
## Run 335 stress 0.2064145 
## Run 336 stress 0.2142442 
## Run 337 stress 0.23795 
## Run 338 stress 0.2096996 
## Run 339 stress 0.2376278 
## Run 340 stress 0.243288 
## Run 341 stress 0.2136044 
## Run 342 stress 0.2112818 
## Run 343 stress 0.226314 
## Run 344 stress 0.2192498 
## Run 345 stress 0.2121882 
## Run 346 stress 0.2236008 
## Run 347 stress 0.2051678 
## ... Procrustes: rmse 0.006828148  max resid 0.04837384 
## Run 348 stress 0.2307428 
## Run 349 stress 0.2442202 
## Run 350 stress 0.2209855 
## Run 351 stress 0.2087844 
## Run 352 stress 0.2363155 
## Run 353 stress 0.2100822 
## Run 354 stress 0.2123063 
## Run 355 stress 0.2196179 
## Run 356 stress 0.2094953 
## Run 357 stress 0.2093195 
## Run 358 stress 0.240378 
## Run 359 stress 0.242381 
## Run 360 stress 0.2062572 
## Run 361 stress 0.2094674 
## Run 362 stress 0.2374466 
## Run 363 stress 0.2051091 
## ... Procrustes: rmse 0.006878626  max resid 0.04743889 
## Run 364 stress 0.2097124 
## Run 365 stress 0.2159819 
## Run 366 stress 0.2105228 
## Run 367 stress 0.2394005 
## Run 368 stress 0.2061732 
## Run 369 stress 0.2254161 
## Run 370 stress 0.2368589 
## Run 371 stress 0.2236687 
## Run 372 stress 0.2157175 
## Run 373 stress 0.2272161 
## Run 374 stress 0.2250152 
## Run 375 stress 0.2055175 
## Run 376 stress 0.2251803 
## Run 377 stress 0.2251491 
## Run 378 stress 0.2283529 
## Run 379 stress 0.2126357 
## Run 380 stress 0.2406926 
## Run 381 stress 0.223573 
## Run 382 stress 0.2401881 
## Run 383 stress 0.2360507 
## Run 384 stress 0.207266 
## Run 385 stress 0.2092853 
## Run 386 stress 0.2128868 
## Run 387 stress 0.2055214 
## Run 388 stress 0.2339978 
## Run 389 stress 0.2136176 
## Run 390 stress 0.2084914 
## Run 391 stress 0.211325 
## Run 392 stress 0.2370026 
## Run 393 stress 0.2315139 
## Run 394 stress 0.2218907 
## Run 395 stress 0.2396513 
## Run 396 stress 0.2063306 
## Run 397 stress 0.2088178 
## Run 398 stress 0.208361 
## Run 399 stress 0.2158339 
## Run 400 stress 0.2098593 
## Run 401 stress 0.2275321 
## Run 402 stress 0.2407526 
## Run 403 stress 0.2104139 
## Run 404 stress 0.2425808 
## Run 405 stress 0.2257607 
## Run 406 stress 0.2246578 
## Run 407 stress 0.2068981 
## Run 408 stress 0.2342482 
## Run 409 stress 0.2301335 
## Run 410 stress 0.2329696 
## Run 411 stress 0.2136081 
## Run 412 stress 0.2396381 
## Run 413 stress 0.2089944 
## Run 414 stress 0.2337164 
## Run 415 stress 0.213777 
## Run 416 stress 0.2079855 
## Run 417 stress 0.215946 
## Run 418 stress 0.2080502 
## Run 419 stress 0.2277592 
## Run 420 stress 0.2152816 
## Run 421 stress 0.2079431 
## Run 422 stress 0.2352768 
## Run 423 stress 0.2270459 
## Run 424 stress 0.2057165 
## Run 425 stress 0.214811 
## Run 426 stress 0.2147054 
## Run 427 stress 0.2252102 
## Run 428 stress 0.2074843 
## Run 429 stress 0.2414707 
## Run 430 stress 0.2260833 
## Run 431 stress 0.2173618 
## Run 432 stress 0.2281661 
## Run 433 stress 0.2305255 
## Run 434 stress 0.2090583 
## Run 435 stress 0.2361227 
## Run 436 stress 0.2051091 
## ... Procrustes: rmse 0.006879076  max resid 0.0474466 
## Run 437 stress 0.209293 
## Run 438 stress 0.2378095 
## Run 439 stress 0.2072412 
## Run 440 stress 0.2357206 
## Run 441 stress 0.2366507 
## Run 442 stress 0.2247267 
## Run 443 stress 0.2094981 
## Run 444 stress 0.2334398 
## Run 445 stress 0.2089173 
## Run 446 stress 0.2086638 
## Run 447 stress 0.2125608 
## Run 448 stress 0.2277668 
## Run 449 stress 0.2078285 
## Run 450 stress 0.2288402 
## Run 451 stress 0.22752 
## Run 452 stress 0.2067795 
## Run 453 stress 0.237524 
## Run 454 stress 0.2146691 
## Run 455 stress 0.206898 
## Run 456 stress 0.2083548 
## Run 457 stress 0.2289782 
## Run 458 stress 0.2102644 
## Run 459 stress 0.2253871 
## Run 460 stress 0.2306201 
## Run 461 stress 0.2065009 
## Run 462 stress 0.2122673 
## Run 463 stress 0.2105119 
## Run 464 stress 0.2090584 
## Run 465 stress 0.21165 
## Run 466 stress 0.2403189 
## Run 467 stress 0.2297095 
## Run 468 stress 0.2347849 
## Run 469 stress 0.2083297 
## Run 470 stress 0.2238213 
## Run 471 stress 0.2086127 
## Run 472 stress 0.2085213 
## Run 473 stress 0.2333247 
## Run 474 stress 0.2138695 
## Run 475 stress 0.2065009 
## Run 476 stress 0.2090532 
## Run 477 stress 0.2093304 
## Run 478 stress 0.2153754 
## Run 479 stress 0.2264083 
## Run 480 stress 0.2315219 
## Run 481 stress 0.234165 
## Run 482 stress 0.2326138 
## Run 483 stress 0.208678 
## Run 484 stress 0.2380717 
## Run 485 stress 0.2268063 
## Run 486 stress 0.2114783 
## Run 487 stress 0.2123347 
## Run 488 stress 0.2264854 
## Run 489 stress 0.2334978 
## Run 490 stress 0.2384193 
## Run 491 stress 0.2375271 
## Run 492 stress 0.2174159 
## Run 493 stress 0.2095724 
## Run 494 stress 0.2303759 
## Run 495 stress 0.2080237 
## Run 496 stress 0.2090355 
## Run 497 stress 0.2076158 
## Run 498 stress 0.2100446 
## Run 499 stress 0.2442039 
## Run 500 stress 0.2054774 
## *** No convergence -- monoMDS stopping criteria:
##     34: no. of iterations >= maxit
##    462: stress ratio > sratmax
##      4: scale factor of the gradient < sfgrmin

Permanova confirms no correlation

## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = p.bc ~ genotype, data = as(sample_data(ps.rel.filtered), "data.frame"))
##          Df SumOfSqs      R2      F Pr(>F)
## genotype  2    0.506 0.01975 0.7356  0.818
## Residual 73   25.111 0.98025              
## Total    75   25.617 1.00000